A Sentiment Analysis Approach to Predicting Stock Returns

Pick up the New York Times and skim over the business section. As you read, you form opinions about the character and prospects of the myriad companies featured in the daily news. Your brain arrives at a “sentiment” score based on a rubric of positive, negative, or neutral emotions stimulated by the text. In the computer science equivalent of reading the news, sentiment analysis is the systematic processing of attributes from words extracted from text mining.

What is clear from looking at a page in the newspaper, text heavily outnumbers numerical information. Charts and graphs are outmatched by anecdotes, recollections, and quotes. Financial analysis, previously constrained to price ratios and margins, is currently undergoing a sentiment revolution.

“Sentiment Analysis in Finance” now has 661,000 search results on Google Scholar, with seminal publications released by Tetlock et al. (2008), Mitra et al. (2008), and Leinweber and Sisk (2010). As shown in the diagram to the left, text is tokenized (broken into words), filtered, stemmed, and classified. The literature has now accessed varied sources of text to such as (a) forums, blogs, and wikis; (b) news and research reports; and (c) content generated by firms.

The Need
Existing academia is chiefly focused on using sentiment to auger stock market returns. As a result, the literature…